Abstract: Post-stall maneuver capability is an important feature of advanced aircrafts, and the extension of flight envelope exceeded the measurement range of traditional air data systems. air data including angle of attack, angle of sideslip, total and static pressure are the essential factors for the control of advanced fighters in post-stall maneuver. An air data fusion and estimation method was proposed and validated based on a thrust vector technology test. The first step is to calculate air data by blending vectors of wind speed, ground speed, and navigation parameters such as attitude and rotation rate. A kind of deep neural network with multiple hidden layers and better feature expression and ability to model complex mapping was designed, trained and employed to fit the calculated angle of attack errors with strong nonlinearity and hysteresis, which were due to the unsteady flow around the aircraft and nonlinear relationship among the flight state parameters. Simulation and flight data shown that the proposed method can complete the estimation of air data even at high angle of attack maneuver and achieve angle of attack parameter with an error no more than 2.3°. The estimated air data can be provided to the flight control system as reliable state feedback.

Key words: post-stall maneuver, high angle of atteck, air data, wind estimation, inertial navigation, deep neural network